library(scales)
Attaching package: 'scales'
The following object is masked from 'package:readr':
col_factor
The following object is masked from 'package:viridis':
viridis_pal
93 MCF7_mapping_histogram.tsv 91 HeLaS3_mapping_histogram.tsv 83 H9hESC_mapping_histogram.tsv 61 HepG2_mapping_histogram.tsv 50 K562_mapping_histogram.tsv 35 BCell_mapping_histogram.tsv 11 FANTOM6DermalFibroblasts_mapping_histogram.tsv 11 VillousMesenchymeAniso2_mapping_histogram.tsv 10 OsteoblastAniso2_mapping_histogram.tsv 9 CD14PositiveMonocyte_mapping_histogram.tsv 8 OsteoblastAniso1_mapping_histogram.tsv 7 FibroblastDermisAniso1_mapping_histogram.tsv 6 VillousMesenchymeAniso1_mapping_histogram.tsv 5 ErythroidProgenitor_mapping_histogram.tsv 5 FANTOM6DermalLymphaticVascularEndothelialCells_mapping_histogram.tsv 5 SkeletalMuscleSatelliteAniso2_mapping_histogram.tsv 4 BronchialSmoothMuscleCells_mapping_histogram.tsv 4 FibroblastDermisAniso2_mapping_histogram.tsv 4 KeratinocyteProgenitor_mapping_histogram.tsv 3 FANTOM6DermalBloodVascularEndothelialCells_mapping_histogram.tsv 3 GM12878_mapping_histogram.tsv 2 HematopoieticProgenitor_mapping_histogram.tsv 2 KeratinocyteFemale_mapping_histogram.tsv 2 UmbilicalVeinCytosolicFraction_mapping_histogram.tsv 1 ArticularChondrocyteKneeJoint_mapping_histogram.tsv 1 MyeloidProgenitor_mapping_histogram.tsv 1 SkeletalMuscleSatelliteAniso1_mapping_histogram.tsv #
<!-- rnb-source-begin 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 -->
```r
#mapping.histogram.path = './MCF7_mapping_histogram.tsv'
mapping.histogram.path = './HeLaS3_mapping_histogram.tsv'
#mapping.histogram.path = './HepG2_mapping_histogram.tsv'
#mapping.histogram.path = './K562_mapping_histogram.tsv'
#mapping.histogram.path = './BCell_mapping_histogram.tsv'
#mapping.histogram.path = './FANTOM6DermalFibroblasts_mapping_histogram.tsv'
#mapping.histogram.path = './VillousMesenchymeAniso2_mapping_histogram.tsv'
#mapping.histogram.path = './OsteoblastAniso2_mapping_histogram.tsv'
#mapping.histogram.path = './CD14PositiveMonocyte_mapping_histogram.tsv'
mapping.histogram = read.delim(mapping.histogram.path, header = F, sep=\\t\, stringsAsFactors = F)
mapping.histogram[\V1\] = mapping.histogram[\V1\] + 1
```
<!-- rnb-source-end -->
```r
#mapping.histogram.path = './MCF7_mapping_histogram.tsv'
mapping.histogram.path = './HeLaS3_mapping_histogram.tsv'
#mapping.histogram.path = './HepG2_mapping_histogram.tsv'
#mapping.histogram.path = './K562_mapping_histogram.tsv'
#mapping.histogram.path = './BCell_mapping_histogram.tsv'
#mapping.histogram.path = './FANTOM6DermalFibroblasts_mapping_histogram.tsv'
#mapping.histogram.path = './VillousMesenchymeAniso2_mapping_histogram.tsv'
#mapping.histogram.path = './OsteoblastAniso2_mapping_histogram.tsv'
#mapping.histogram.path = './CD14PositiveMonocyte_mapping_histogram.tsv'
mapping.histogram = read.delim(mapping.histogram.path, header = F, sep=\\t\, stringsAsFactors = F)
mapping.histogram[\V1\] = mapping.histogram[\V1\] + 1
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
ggplot(mapping.histogram, aes(x = V1)) +
theme_classic() +
geom_histogram(binwidth = 5,
fill = \#00BFC4\,
color = \#00BFC4\) +
labs(title = \Number of alignments for centromeric reads\,
x = \Frequency\,
y = \Count\) #+
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2dwbG90KG1hcHBpbmcuaGlzdG9ncmFtLCBhZXMoeCA9IFYxKSkgK1xuICB0aGVtZV9jbGFzc2ljKCkgK1xuICBnZW9tX2hpc3RvZ3JhbShiaW53aWR0aCA9IDUsXG4gICAgICAgICAgICAgICAgIGZpbGwgPSBcXCMwMEJGQzRcXCxcbiAgICAgICAgICAgICAgICAgY29sb3IgPSBcXCMwMEJGQzRcXCkgK1xuICBsYWJzKHRpdGxlID0gXFxOdW1iZXIgb2YgYWxpZ25tZW50cyBmb3IgY2VudHJvbWVyaWMgcmVhZHNcXCxcbiAgICAgICB4ID0gXFxGcmVxdWVuY3lcXCxcbiAgICAgICB5ID0gXFxDb3VudFxcKSAjK1xuYGBgXG5gYGAifQ== -->
```r
```r
ggplot(mapping.histogram, aes(x = V1)) +
theme_classic() +
geom_histogram(binwidth = 5,
fill = \#00BFC4\,
color = \#00BFC4\) +
labs(title = \Number of alignments for centromeric reads\,
x = \Frequency\,
y = \Count\) #+
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1wbG90LWJlZ2luIGV5Sm9aV2xuYUhRaU9qUXdNaTR6TkRnMkxDSjNhV1IwYUNJNk5qVXhMQ0p6YVhwbFgySmxhR0YyYVc5eUlqb3dMQ0pqYjI1a2FYUnBiMjV6SWpwYlhYMD0gLS0+XG5cbjxpbWcgc3JjPVxcZGF0YTppbWFnZS9wbmc7YmFzZTY0XG4ifQ== -->
```
<!-- rnb-plot-begin eyJoZWlnaHQiOjQwMi4zNDg2LCJ3aWR0aCI6NjUxLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->
<img src=\data:image/png;base64
```
<!-- rnb-output-end -->
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\" />\n\n<!-- rnb-plot-end -->\n"} -->
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" />
<!-- rnb-plot-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVJQ0FqYzJOaGJHVmZlVjlzYjJjeE1DZ3BJQ3RjYmlBZ0kzUm9aVzFsS0dGNGFYTXVkR1Y0ZEM1NElEMGdaV3hsYldWdWRGOTBaWGgwS0dGdVoyeGxJRDBnTkRVc0lHaHFkWE4wSUQwZ01Ta3BYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbiAgI3NjYWxlX3lfbG9nMTAoKSArXG4gICN0aGVtZShheGlzLnRleHQueCA9IGVsZW1lbnRfdGV4dChhbmdsZSA9IDQ1LCBoanVzdCA9IDEpKVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
#scale_y_log10() +
#theme(axis.text.x = element_text(angle = 45, hjust = 1))
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dUlDQWpjMk5oYkdWZmVWOXNiMmN4TUNncElDdGNiaUFnSTNSb1pXMWxLR0Y0YVhNdWRHVjRkQzU0SUQwZ1pXeGxiV1Z1ZEY5MFpYaDBLR0Z1WjJ4bElEMGdORFVzSUdocWRYTjBJRDBnTVNrcFhHNWdZR0JjYm1CZ1lDSjkgLS0+XG5cbmBgYHJcbmBgYHJcbiAgI3NjYWxlX3lfbG9nMTAoKSArXG4gICN0aGVtZShheGlzLnRleHQueCA9IGVsZW1lbnRfdGV4dChhbmdsZSA9IDQ1LCBoanVzdCA9IDEpKVxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuICAjc2NhbGVfeV9sb2cxMCgpICtcbiAgI3RoZW1lKGF4aXMudGV4dC54ID0gZWxlbWVudF90ZXh0KGFuZ2xlID0gNDUsIGhqdXN0ID0gMSkpXG5gYGBcbmBgYCJ9 -->
```r
```r
#scale_y_log10() +
#theme(axis.text.x = element_text(angle = 45, hjust = 1))
```
```
<!-- rnb-source-end -->
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-->\n\n```r\ndinucleotide.table.1 = read.delim('./K562_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\K562_\\, .), -dinucleotide)\ndinucleotide.table.2 = read.delim('./MCF7_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\MCF7_\\, .), -dinucleotide)\ndinucleotide.table.3 = read.delim('./HeLaS3_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\HeLaS3_\\, .), -dinucleotide)\ndinucleotide.table.4 = read.delim('./HepG2_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\HepG2_\\, .), -dinucleotide)\ndinucleotide.table.5 = read.delim('./BCell_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\BCell_\\, .), -dinucleotide)\ndinucleotide.table.6 = read.delim('./FANTOM6DermalLymphaticVascularEndothelialCells_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\DermalLymphaticVascularEndothelial_\\, .), -dinucleotide)\ndinucleotide.table.7 = read.delim('./VillousMesenchymeAniso2_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\VillousMesenchyme_\\, .), -dinucleotide)\ndinucleotide.table.8 = read.delim('./MyeloidProgenitor_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\MyeloidProgenitor_\\, .), -dinucleotide)\ndinucleotide.table.9 = read.delim('./CD14PositiveMonocyte_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\CD14Monocyte_\\, .), -dinucleotide)\ndinucleotide.table.10 = read.delim('./H9hESC_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\H9hESC_\\, .), -dinucleotide)\ndinucleotide.table.11 = read.delim('./GM12878_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\GM12878_\\, .), -dinucleotide)\n\ndinucleotide.table = dinucleotide.table.1 %>%\n  left_join(dinucleotide.table.2, by = join_by(dinucleotide)) %>%\n  left_join(dinucleotide.table.3, by = join_by(dinucleotide)) %>%\n  left_join(dinucleotide.table.4, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.5, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.6, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.7, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.8, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.9, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.10, by = join_by(dinucleotide)) %>%\n  left_join(dinucleotide.table.11, by = join_by(dinucleotide))   \n```\n\n<!-- rnb-source-end -->\n"} -->
dinucleotide.table.1 = read.delim('./K562_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\K562_\, .), -dinucleotide)
dinucleotide.table.2 = read.delim('./MCF7_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\MCF7_\, .), -dinucleotide)
dinucleotide.table.3 = read.delim('./HeLaS3_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\HeLaS3_\, .), -dinucleotide)
dinucleotide.table.4 = read.delim('./HepG2_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\HepG2_\, .), -dinucleotide)
dinucleotide.table.5 = read.delim('./BCell_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\BCell_\, .), -dinucleotide)
dinucleotide.table.6 = read.delim('./FANTOM6DermalLymphaticVascularEndothelialCells_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\DermalLymphaticVascularEndothelial_\, .), -dinucleotide)
dinucleotide.table.7 = read.delim('./VillousMesenchymeAniso2_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\VillousMesenchyme_\, .), -dinucleotide)
dinucleotide.table.8 = read.delim('./MyeloidProgenitor_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\MyeloidProgenitor_\, .), -dinucleotide)
dinucleotide.table.9 = read.delim('./CD14PositiveMonocyte_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\CD14Monocyte_\, .), -dinucleotide)
dinucleotide.table.10 = read.delim('./H9hESC_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\H9hESC_\, .), -dinucleotide)
dinucleotide.table.11 = read.delim('./GM12878_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\GM12878_\, .), -dinucleotide)
dinucleotide.table = dinucleotide.table.1 %>%
left_join(dinucleotide.table.2, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.3, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.4, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.5, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.6, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.7, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.8, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.9, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.10, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.11, by = join_by(dinucleotide))
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-->\n\n```r\n```r\ndinucleotide.table.1 = read.delim('./K562_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\K562_\\, .), -dinucleotide)\ndinucleotide.table.2 = read.delim('./MCF7_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\MCF7_\\, .), -dinucleotide)\ndinucleotide.table.3 = read.delim('./HeLaS3_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\HeLaS3_\\, .), -dinucleotide)\ndinucleotide.table.4 = read.delim('./HepG2_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\HepG2_\\, .), -dinucleotide)\ndinucleotide.table.5 = read.delim('./BCell_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\BCell_\\, .), -dinucleotide)\ndinucleotide.table.6 = read.delim('./FANTOM6DermalLymphaticVascularEndothelialCells_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\DermalLymphaticVascularEndothelial_\\, .), -dinucleotide)\ndinucleotide.table.7 = read.delim('./VillousMesenchymeAniso2_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\VillousMesenchyme_\\, .), -dinucleotide)\ndinucleotide.table.8 = read.delim('./MyeloidProgenitor_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\MyeloidProgenitor_\\, .), -dinucleotide)\ndinucleotide.table.9 = read.delim('./CD14PositiveMonocyte_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\CD14Monocyte_\\, .), -dinucleotide)\ndinucleotide.table.10 = read.delim('./H9hESC_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\H9hESC_\\, .), -dinucleotide)\ndinucleotide.table.11 = read.delim('./GM12878_dinucleotide_table.tsv', header = T,  sep=\\\\t\\, stringsAsFactors = F) %>%\n  filter(!(dinucleotide %in% c(\\AN\\, \\CN\\, \\GN\\, \\TN\\))) %>%\n  rename_with(~ paste0(\\GM12878_\\, .), -dinucleotide)\n\ndinucleotide.table = dinucleotide.table.1 %>%\n  left_join(dinucleotide.table.2, by = join_by(dinucleotide)) %>%\n  left_join(dinucleotide.table.3, by = join_by(dinucleotide)) %>%\n  left_join(dinucleotide.table.4, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.5, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.6, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.7, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.8, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.9, by = join_by(dinucleotide))  %>%\n  left_join(dinucleotide.table.10, by = join_by(dinucleotide)) %>%\n  left_join(dinucleotide.table.11, by = join_by(dinucleotide))   \n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
dinucleotide.table.1 = read.delim('./K562_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\K562_\, .), -dinucleotide)
dinucleotide.table.2 = read.delim('./MCF7_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\MCF7_\, .), -dinucleotide)
dinucleotide.table.3 = read.delim('./HeLaS3_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\HeLaS3_\, .), -dinucleotide)
dinucleotide.table.4 = read.delim('./HepG2_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\HepG2_\, .), -dinucleotide)
dinucleotide.table.5 = read.delim('./BCell_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\BCell_\, .), -dinucleotide)
dinucleotide.table.6 = read.delim('./FANTOM6DermalLymphaticVascularEndothelialCells_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\DermalLymphaticVascularEndothelial_\, .), -dinucleotide)
dinucleotide.table.7 = read.delim('./VillousMesenchymeAniso2_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\VillousMesenchyme_\, .), -dinucleotide)
dinucleotide.table.8 = read.delim('./MyeloidProgenitor_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\MyeloidProgenitor_\, .), -dinucleotide)
dinucleotide.table.9 = read.delim('./CD14PositiveMonocyte_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\CD14Monocyte_\, .), -dinucleotide)
dinucleotide.table.10 = read.delim('./H9hESC_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\H9hESC_\, .), -dinucleotide)
dinucleotide.table.11 = read.delim('./GM12878_dinucleotide_table.tsv', header = T, sep=\\t\, stringsAsFactors = F) %>%
filter(!(dinucleotide %in% c(\AN\, \CN\, \GN\, \TN\))) %>%
rename_with(~ paste0(\GM12878_\, .), -dinucleotide)
dinucleotide.table = dinucleotide.table.1 %>%
left_join(dinucleotide.table.2, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.3, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.4, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.5, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.6, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.7, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.8, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.9, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.10, by = join_by(dinucleotide)) %>%
left_join(dinucleotide.table.11, by = join_by(dinucleotide))
```
```
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```r
files = c(
K562 = "K562_dinucleotide_table.tsv",
MCF7 = "MCF7_dinucleotide_table.tsv",
HeLaS3 = "HeLaS3_dinucleotide_table.tsv",
HepG2 = "HepG2_dinucleotide_table.tsv",
BCell = "BCell_dinucleotide_table.tsv",
FANTOM6DermalLymphaticVascularEndothelialCells = "FANTOM6DermalLymphaticVascularEndothelialCells_dinucleotide_table.tsv",
VillousMesenchymeAniso2 = "VillousMesenchymeAniso2_dinucleotide_table.tsv",
MyeloidProgenitor = "MyeloidProgenitor_dinucleotide_table.tsv",
CD14PositiveMonocyte = "CD14PositiveMonocyte_dinucleotide_table.tsv",
H9hESC = "H9hESC_dinucleotide_table.tsv",
GM12878 = "GM12878_dinucleotide_table.tsv",
FANTOM6DermalBloodVascularEndothelialCells = "FANTOM6DermalBloodVascularEndothelialCells_dinucleotide_table.tsv",
ArticularChondrocyteKneeJoint = "ArticularChondrocyteKneeJoint_dinucleotide_table.tsv",
BronchialSmoothMuscleCells = "BronchialSmoothMuscleCells_dinucleotide_table.tsv",
ErythroidProgenitor = "ErythroidProgenitor_dinucleotide_table.tsv",
FANTOM6DermalFibroblasts = "FANTOM6DermalFibroblasts_dinucleotide_table.tsv",
FibroblastDermisAniso1 = "FibroblastDermisAniso1_dinucleotide_table.tsv",
FibroblastDermisAniso2 = "FibroblastDermisAniso2_dinucleotide_table.tsv",
HematopoieticProgenitor = "HematopoieticProgenitor_dinucleotide_table.tsv",
KeratinocyteFemale = "KeratinocyteFemale_dinucleotide_table.tsv",
KeratinocyteProgenitor = "KeratinocyteProgenitor_dinucleotide_table.tsv",
OsteoblastAniso1 = "OsteoblastAniso1_dinucleotide_table.tsv",
OsteoblastAniso2 = "OsteoblastAniso2_dinucleotide_table.tsv",
SkeletalMuscleSatelliteAniso1 = "SkeletalMuscleSatelliteAniso1_dinucleotide_table.tsv",
SkeletalMuscleSatelliteAniso2 = "SkeletalMuscleSatelliteAniso2_dinucleotide_table.tsv",
UmbilicalVeinCytosolicFraction = "UmbilicalVeinCytosolicFraction_dinucleotide_table.tsv",
VillousMesenchymeAniso1 = "VillousMesenchymeAniso1_dinucleotide_table.tsv"
)
read_and_process = function(file, prefix) {
read.delim(file, header = TRUE, sep = "\t", stringsAsFactors = FALSE) %>%
filter(!dinucleotide %in% c("AN", "CN", "GN", "TN")) %>%
rename_with(~ paste0(prefix, "_", .), -dinucleotide)
}
dinucleotide_tables = imap(files, ~ read_and_process(.x, .y))
dinucleotide.table = reduce(dinucleotide_tables, left_join, by = "dinucleotide")
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
m = as.matrix(sapply(dinucleotide.table[,2:ncol(dinucleotide.table)], as.numeric))
rownames(m) = dinucleotide.table$dinucleotide
m[is.na(m)] = 0
#m_g0 = m[rowSums(m) > 0, ]
m_g0 = m[, colSums(m) > 10]
sample_names <- sapply(strsplit(colnames(m_g0), \_\), `[`, 1)
unique_samples <- unique(sample_names)
sample_colors <- setNames(rainbow(length(unique_samples)), unique_samples)
ha <- rowAnnotation(
Sample = sample_names,
col = list(Sample = sample_colors)
)
col_fun = colorRamp2(c(min(m_g0), max(m_g0)/2, max(m_g0)), c(\grey\, \#673ab7\, \#522e93\))
fig1 =
Heatmap(m_g0,
cluster_columns = T,
cluster_rows = F,
row_names_side = \left\,
show_column_names = T,
show_row_dend = FALSE,
show_column_dend = FALSE,
column_names_side = \top\,
row_dend_side = \left\,
col = col_fun,
row_names_gp = gpar(fontsize = 15),
column_names_gp = gpar(fontsize = 2),
heatmap_legend_param = list(title = \Number of alignments\))
#+ ha
fig1 = draw(fig1)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
m = as.matrix(sapply(dinucleotide.table[,2:ncol(dinucleotide.table)], as.numeric))
rownames(m) = dinucleotide.table$dinucleotide
m[is.na(m)] = 0
#m_g0 = m[rowSums(m) > 0, ]
m_g0 = m[, colSums(m) > 10]
sample_names <- sapply(strsplit(colnames(m_g0), \_\), `[`, 1)
unique_samples <- unique(sample_names)
sample_colors <- setNames(rainbow(length(unique_samples)), unique_samples)
ha <- rowAnnotation(
Sample = sample_names,
col = list(Sample = sample_colors)
)
col_fun = colorRamp2(c(min(m_g0), max(m_g0)/2, max(m_g0)), c(\grey\, \#673ab7\, \#522e93\))
fig1 =
Heatmap(m_g0,
cluster_columns = T,
cluster_rows = F,
row_names_side = \left\,
show_column_names = T,
show_row_dend = FALSE,
show_column_dend = FALSE,
column_names_side = \top\,
row_dend_side = \left\,
col = col_fun,
row_names_gp = gpar(fontsize = 15),
column_names_gp = gpar(fontsize = 2),
heatmap_legend_param = list(title = \Number of alignments\))
#+ ha
fig1 = draw(fig1)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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```
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<img src=\data:image/png;base64
```
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\" 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" />
<!-- rnb-plot-end -->
<!-- rnb-output-end -->
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#Кластеризовать по столбцам
# break if 0
# конкатенировать название библиотеки к ID рида и попробовать кластеризовать
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```r
```r
#Кластеризовать по столбцам
# break if 0
# конкатенировать название библиотеки к ID рида и попробовать кластеризовать
```
```
<!-- rnb-source-end -->
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```r
m <- as.matrix(sapply(dinucleotide.table[, 2:ncol(dinucleotide.table)], as.numeric))
rownames(m) <- dinucleotide.table$dinucleotide
#m <- as.matrix(sapply(dinucleotide.table_filtered[, 2:ncol(dinucleotide.table_filtered)], as.numeric))
#rownames(m) <- dinucleotide.table_filtered$dinucleotide
m[is.na(m)] <- 0
m_g0 <- m[, colSums(m) >= 10]
sample_names <- sapply(strsplit(colnames(m_g0), "_"), `[`, 1)
unique_samples <- unique(sample_names)
sample_colors <- setNames(rainbow(length(unique_samples)), unique_samples)
ha <- columnAnnotation(
Sample = sample_names,
col = list(Sample = sample_colors)
)
col_fun <- colorRamp2(c(min(m_g0), max(m_g0) / 2, max(m_g0)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
m_g0 <- m_g0[order(rowSums(m_g0), decreasing = TRUE), ]
fig1 <- Heatmap(
m_g0,
cluster_columns = TRUE,
cluster_rows = FALSE,
clustering_distance_columns = "pearson", # pretty good
# clustering_distance_columns = "spearman", # bad
# clustering_distance_columns = "kendall", # bad
#clustering_distance_columns = "euclidean", # default
# clustering_distance_columns = "maximum", # bad
# clustering_distance_columns = "manhattan",# pretty good
# clustering_distance_columns = "canberra", # pretty good
# clustering_distance_columns = "binary", # pretty good
# clustering_distance_columns = "minkowski",# pretty good
row_names_side = "left",
show_column_names = FALSE,
show_row_dend = FALSE,
show_column_dend = TRUE,
column_names_side = "top",
row_dend_side = "left",
col = col_fun,
row_names_gp = gpar(fontsize = 15),
column_names_gp = gpar(fontsize = 2),
heatmap_legend_param = list(title = "Number of alignments"),
top_annotation = ha
)
fig1 <- draw(fig1, annotation_legend_side = "right")
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-plot-begin eyJoZWlnaHQiOjM4OS45ODc2LCJ3aWR0aCI6NjMxLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->\n\n<img 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\" 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" />
<!-- rnb-plot-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjM1Z0S0cwcFhHNWNibUJnWUNKOSAtLT5cblxuYGBgclxuc3VtKG0pXG5cbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3VtKG0pXG5cbmBgYCJ9 -->
```r
sum(m)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQ1NTM2NlxuIn0= -->
```
[1] 455366
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjM1Z0S0hObGJHVmpkQ2hrYVc1MVkyeGxiM1JwWkdVdWRHRmliR1ZmWm1sc2RHVnlaV1FzSUhkb1pYSmxLR2x6TG01MWJXVnlhV01wS1NsY2JseHVZR0JnSW4wPSAtLT5cblxuYGBgclxuc3VtKHNlbGVjdChkaW51Y2xlb3RpZGUudGFibGVfZmlsdGVyZWQsIHdoZXJlKGlzLm51bWVyaWMpKSlcblxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3VtKHNlbGVjdChkaW51Y2xlb3RpZGUudGFibGVfZmlsdGVyZWQsIHdoZXJlKGlzLm51bWVyaWMpKSlcblxuYGBgIn0= -->
```r
sum(select(dinucleotide.table_filtered, where(is.numeric)))
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDM1NTAyMVxuIn0= -->
```
[1] 355021
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
col_names = colnames(dinucleotide.table_filtered)
output <- tibble(
sample_colors = sub(\_.*\, \\, col_names),
read_id = sub(\^[^_]+_\, \\, col_names)
)
output = output[-1,]
write_tsv(output, \/Users/pavel/Desktop/stable_dinucleotides_reads.tsv\, col_names = FALSE)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuY29sX25hbWVzID0gY29sbmFtZXMoZGludWNsZW90aWRlLnRhYmxlX2ZpbHRlcmVkKVxuXG5vdXRwdXQgPC0gdGliYmxlKFxuICBzYW1wbGVfY29sb3JzID0gc3ViKFxcXy4qXFwsIFxcXFwsIGNvbF9uYW1lcyksXG4gIHJlYWRfaWQgPSBzdWIoXFxeW15fXStfXFwsIFxcXFwsIGNvbF9uYW1lcylcbilcblxub3V0cHV0ID0gb3V0cHV0Wy0xLF1cblxud3JpdGVfdHN2KG91dHB1dCwgXFwvVXNlcnMvcGF2ZWwvRGVza3RvcC9zdGFibGVfZGludWNsZW90aWRlc19yZWFkcy50c3ZcXCwgY29sX25hbWVzID0gRkFMU0UpXG5gYGBcbmBgYCJ9 -->
```r
```r
col_names = colnames(dinucleotide.table_filtered)
output <- tibble(
sample_colors = sub(\_.*\, \\, col_names),
read_id = sub(\^[^_]+_\, \\, col_names)
)
output = output[-1,]
write_tsv(output, \/Users/pavel/Desktop/stable_dinucleotides_reads.tsv\, col_names = FALSE)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjR1JtS0Z3aUwxVnpaWEp6TDNCaGRtVnNMMFJsYzJ0MGIzQXZhVzVwZEdsaGRHOXljMTlvWldGMGJXRndMbkJrWmx3aUxDQjNhV1IwYUNBOUlERTFMQ0JvWldsbmFIUWdQU0EzS1Z4dVpISmhkeWhtYVdjeEtWeHVaR1YyTG05bVppZ3BYRzVnWUdBaWZRPT0gLS0+XG5cbmBgYHJcbnBkZihcIi9Vc2Vycy9wYXZlbC9EZXNrdG9wL2luaXRpYXRvcnNfaGVhdG1hcC5wZGZcIiwgd2lkdGggPSAxNSwgaGVpZ2h0ID0gNylcbmRyYXcoZmlnMSlcbmRldi5vZmYoKVxuYGBgXG5cbjwhLS0gcm5iLXNvdXJjZS1lbmQgLS0+XG4ifQ== -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGRmKFwiL1VzZXJzL3BhdmVsL0Rlc2t0b3AvaW5pdGlhdG9yc19oZWF0bWFwLnBkZlwiLCB3aWR0aCA9IDE1LCBoZWlnaHQgPSA3KVxuZHJhdyhmaWcxKVxuZGV2Lm9mZigpXG5gYGAifQ== -->
```r
pdf("/Users/pavel/Desktop/initiators_heatmap.pdf", width = 15, height = 7)
draw(fig1)
dev.off()
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->
```
null device
1
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI2Rpc3RhbmNlcy5oaXN0b2dyYW0ucGF0aCA9ICcuL0hlTGFTM19kaXN0YW5jZXNfZnJvbV9zdGFydC50c3YnXG5kaXN0YW5jZXMuaGlzdG9ncmFtLnBhdGggPSAnLi9IZUxhUzMud2hvbGVfY2VudHJvbWVyZS4xR19YTV85MC5ub190cmFuc3Bvc29ucy5tZXRhcGxvdF90c3NfbW9ub21lcnMudHN2J1xuZGlzdGFuY2VzLmhpc3RvZ3JhbSA9IHJlYWQuZGVsaW0oZGlzdGFuY2VzLmhpc3RvZ3JhbS5wYXRoLCBoZWFkZXIgPSBULCAgc2VwPVwiXFx0XCIsIHN0cmluZ3NBc0ZhY3RvcnMgPSBGKVxuYGBgIn0= -->
```r
#distances.histogram.path = './HeLaS3_distances_from_start.tsv'
distances.histogram.path = './HeLaS3.whole_centromere.1G_XM_90.no_transposons.metaplot_tss_monomers.tsv'
distances.histogram = read.delim(distances.histogram.path, header = T, sep="\t", stringsAsFactors = F)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiV2FybmluZyBpbiBmaWxlKGZpbGUsIFwicnRcIikgOlxuICBjYW5ub3Qgb3BlbiBmaWxlICcuL0hlTGFTMy53aG9sZV9jZW50cm9tZXJlLjFHX1hNXzkwLm5vX3RyYW5zcG9zb25zLm1ldGFwbG90X3Rzc19tb25vbWVycy50c3YnOiBObyBzdWNoIGZpbGUgb3IgZGlyZWN0b3J5XG5FcnJvciBpbiBmaWxlKGZpbGUsIFwicnRcIikgOiBjYW5ub3Qgb3BlbiB0aGUgY29ubmVjdGlvblxuIn0= -->
```
Warning in file(file, "rt") :
cannot open file './HeLaS3.whole_centromere.1G_XM_90.no_transposons.metaplot_tss_monomers.tsv': No such file or directory
Error in file(file, "rt") : cannot open the connection
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVJeWhwY3k1dVlTaGthWE4wWVc1alpYTXVhR2x6ZEc5bmNtRnRXMXdpY21Wc1lYUnBkbVZmY0c5emFYUnBiMjVjSWwwcEtWeHVkbWxsZHloa2FYTjBZVzVqWlhNdWFHbHpkRzluY21GdElDVStKVnh1SUNCbWFXeDBaWElvSVdsekxtNWhLSEpsYkdGMGFYWmxYM0J2YzJsMGFXOXVLU2twSUZ4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG4jKGlzLm5hKGRpc3RhbmNlcy5oaXN0b2dyYW1bXFxyZWxhdGl2ZV9wb3NpdGlvblxcXSkpXG52aWV3KGRpc3RhbmNlcy5oaXN0b2dyYW0gJT4lXG4gIGZpbHRlcighaXMubmEocmVsYXRpdmVfcG9zaXRpb24pKSkgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
#(is.na(distances.histogram[\relative_position\]))
view(distances.histogram %>%
filter(!is.na(relative_position)))
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZR0JnY2x4dUl5aHBjeTV1WVNoa2FYTjBZVzVqWlhNdWFHbHpkRzluY21GdFcxeGNjbVZzWVhScGRtVmZjRzl6YVhScGIyNWNYRjBwS1Z4dWRtbGxkeWhrYVhOMFlXNWpaWE11YUdsemRHOW5jbUZ0SUNVK0pWeHVJQ0JtYVd4MFpYSW9JV2x6TG01aEtISmxiR0YwYVhabFgzQnZjMmwwYVc5dUtTa3BJRnh1WUdCZ1hHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxuYGBgclxuIyhpcy5uYShkaXN0YW5jZXMuaGlzdG9ncmFtW1xccmVsYXRpdmVfcG9zaXRpb25cXF0pKVxudmlldyhkaXN0YW5jZXMuaGlzdG9ncmFtICU+JVxuICBmaWx0ZXIoIWlzLm5hKHJlbGF0aXZlX3Bvc2l0aW9uKSkpIFxuYGBgXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyhpcy5uYShkaXN0YW5jZXMuaGlzdG9ncmFtW1xccmVsYXRpdmVfcG9zaXRpb25cXF0pKVxudmlldyhkaXN0YW5jZXMuaGlzdG9ncmFtICU+JVxuICBmaWx0ZXIoIWlzLm5hKHJlbGF0aXZlX3Bvc2l0aW9uKSkpIFxuYGBgXG5gYGAifQ== -->
```r
```r
#(is.na(distances.histogram[\relative_position\]))
view(distances.histogram %>%
filter(!is.na(relative_position)))
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
distances.histogram.path = './HeLaS3_distances_from_start.tsv'
distances.histogram = read.delim(distances.histogram.path, header = F, sep=\;\, stringsAsFactors = F)
ggplot(distances.histogram, aes(x = V2, y = V1)) +
theme_classic() +
#geom_point() +
geom_bar(stat=\identity\) +
labs(x = \Distance from end of the monomer\,
y = \Count\)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
distances.histogram.path = './HeLaS3_distances_from_start.tsv'
distances.histogram = read.delim(distances.histogram.path, header = F, sep=\;\, stringsAsFactors = F)
ggplot(distances.histogram, aes(x = V2, y = V1)) +
theme_classic() +
#geom_point() +
geom_bar(stat=\identity\) +
labs(x = \Distance from end of the monomer\,
y = \Count\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
distances.histogram.path = './HeLaS3_distances_start_HOR.csv'
distances.histogram = read.delim(distances.histogram.path, header = F, sep=\;\, stringsAsFactors = F)
ggplot(distances.histogram, aes(x = V2, y = V1)) +
theme_classic() +
#geom_point() +
geom_bar(stat=\identity\, width = 1000000) +
labs(x = \Distance from end of the monomer\,
y = \Count\)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZGlzdGFuY2VzLmhpc3RvZ3JhbS5wYXRoID0gJy4vSGVMYVMzX2Rpc3RhbmNlc19zdGFydF9IT1IuY3N2J1xuZGlzdGFuY2VzLmhpc3RvZ3JhbSA9IHJlYWQuZGVsaW0oZGlzdGFuY2VzLmhpc3RvZ3JhbS5wYXRoLCBoZWFkZXIgPSBGLCAgc2VwPVxcO1xcLCBzdHJpbmdzQXNGYWN0b3JzID0gRilcbmdncGxvdChkaXN0YW5jZXMuaGlzdG9ncmFtLCBhZXMoeCA9IFYyLCB5ID0gVjEpKSArXG4gIHRoZW1lX2NsYXNzaWMoKSArXG4gICNnZW9tX3BvaW50KCkgK1xuICBnZW9tX2JhcihzdGF0PVxcaWRlbnRpdHlcXCwgd2lkdGggPSAxMDAwMDAwKSArXG4gIGxhYnMoeCA9IFxcRGlzdGFuY2UgZnJvbSBlbmQgb2YgdGhlIG1vbm9tZXJcXCxcbiAgICAgICB5ID0gXFxDb3VudFxcKVxuYGBgXG5gYGAifQ== -->
```r
```r
distances.histogram.path = './HeLaS3_distances_start_HOR.csv'
distances.histogram = read.delim(distances.histogram.path, header = F, sep=\;\, stringsAsFactors = F)
ggplot(distances.histogram, aes(x = V2, y = V1)) +
theme_classic() +
#geom_point() +
geom_bar(stat=\identity\, width = 1000000) +
labs(x = \Distance from end of the monomer\,
y = \Count\)
```
```
<!-- rnb-source-end -->
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<!-- rnb-text-begin -->
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<!-- rnb-output-begin 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 -->
dinucleotide.table %>%
filter(dinucleotide == \GA\) %>%
t() %>%
as.data.frame() %>%
filter(V1 != \GA\) %>%
filter(V1 > 0)
````
```r
dinucleotide.table %>%
filter(dinucleotide == \GA\) %>%
t() %>%
as.data.frame() %>%
filter(V1 != \GA\) %>%
filter(V1 > 0)
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
# Analysis of reads with stable dinucleotides
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
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```r
monomers.path = '/Users/pavel/Desktop/adjusted_alpha_monomers_full_ranges.bed'
monomers = read.delim(monomers.path, header = FALSE)
monomers = monomers[,-6]
colnames(monomers)[1:5] = c("chr",
"start",
"end",
"type",
"strand")
monomers = monomers %>%
mutate(length = end - start)
monomers %>%
group_by(type, strand) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(type) %>%
filter(type %in% c("D1", "D2", "J1", "J2", "W1")) %>%
mutate(strand = factor(strand, levels = c("+", "-"))) %>%
ggplot(aes(x = type, y = count, fill = strand)) +
geom_bar(stat = "identity", position = "dodge") +
theme_classic(base_size = 20) +
labs(x = "Monomer type",
y = "Count",
fill = "Strand")
`summarise()` has grouped output by 'type'. You can override using the `.groups`
argument.
ggsave(filename = paste0("/Users/pavel/Desktop/","monomer_strand_count.pdf"),width = 7,height=5)
ggplot(monomers, aes(x = type, y = length, fill = type, color = type)) +
#geom_violin(trim = FALSE) +
geom_jitter(width = 0.2, alpha = 0.1) +
scale_y_continuous(breaks = seq(0, 200, 10), limits = c(0, 200)) +
# (155, 175)
labs(x = "Monomer Type",
y = "Length") +
theme_classic() +
theme(legend.position = "none")
# Рисовать постолбцовые пропорции
monomers <- monomers %>%
mutate(type = as.character(unlist(type)))
length_heatmap_data <- monomers %>%
filter(length >= 160, length <= 175) %>%
count(length, type) %>%
pivot_wider(names_from = type, values_from = n, values_fill = list(n = 0)) %>%
arrange(desc(length)) %>%
column_to_rownames("length")
length_matrix <- as.matrix(length_heatmap_data)
length_matrix <- length_matrix[, sort(colnames(length_matrix))]
lengths <- as.integer(rownames(length_matrix))
row_labels_to_show <- ifelse(lengths %% 1 == 0, as.character(lengths), "")
length_matrix <- sweep(length_matrix, 2, colSums(length_matrix), FUN = "/")
col_fun = colorRamp2(c(0, 0.5, 1), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
#col_fun = colorRamp2(c(min(length_matrix), max(length_matrix) / 5, max(length_matrix)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
fig2 = Heatmap(length_matrix,
name = "Column-wise proportion",
col = col_fun,
cluster_rows = FALSE,
cluster_columns = FALSE,
row_title = "Length",
column_title = "Monomer Type",
row_names_side = "left",
row_labels = row_labels_to_show)
draw(fig2)
pdf("/Users/pavel/Desktop/monomer_lengths_heatmap.pdf", width = 12, height = 7)
draw(fig2)
dev.off()
null device
1
# Clean of transposon signal
transposons.path = "/Users/pavel/Desktop/3_to_1/centromere_igv/adjusted_centromeric_transposons_full_ranges.bed"
transposons = read.delim(transposons.path, header = FALSE)
colnames(transposons)[1:6] = c("chr",
"start",
"end",
"type",
"strand",
"nan")
transposons = transposons %>%
mutate(start500 = start - 500) %>%
mutate(end500 = end + 500)
# Load the table
stable.reads.path = "/Users/pavel/Desktop/stable_reads_intersected.tsv"
stable.reads = read.delim(stable.reads.path, header = FALSE)
colnames(stable.reads)[1:12] = c("sample",
"monomer_chr",
"monomer_start",
"monomer_end",
"monomer_type",
"monomer_strand",
"initiator_chr",
"initiator_start",
"initiator_end",
"read_name",
"read_strand",
"initiator_dinucleotide")
stable.reads = stable.reads %>%
mutate(read_strand = ifelse(read_strand == "+", "-",
ifelse(read_strand == "-", "+", read_strand))) %>%
mutate(sample_readname = paste(sample, read_name, sep = "_"))
transposons_ranges = transposons %>%
select(chr, start500, end500)
stable_reads_ranges = stable.reads %>%
select(sample_readname, initiator_chr, initiator_start, initiator_end)
overlapping = fuzzy_inner_join(
stable_reads_ranges,
transposons_ranges,
by = c("initiator_chr" = "chr",
"initiator_start" = "start500",
"initiator_end" = "end500"),
match_fun = list(`==`, `>=`, `<=`)
)
stable.reads = stable.reads %>%
filter(!sample_readname %in% overlapping$sample_readname)
stable.reads %>%
select(sample_readname) %>%
arrange() %>%
unique() %>%
summarise(count = n())
stable.reads.f = stable.reads %>%
group_by(sample_readname) %>%
mutate(freq = ave(seq_along(initiator_dinucleotide), initiator_dinucleotide, FUN = length)) %>%
group_by(sample_readname, initiator_dinucleotide) %>%
mutate(dinuc_count = n()) %>%
ungroup() %>%
group_by(sample_readname) %>%
filter(dinuc_count == max(dinuc_count)) %>%
select(-freq, -dinuc_count) %>%
ungroup()
stable.reads.matrix = stable.reads.f %>%
group_by(monomer_type, sample_readname) %>%
summarise(count = n(), .groups = "drop") %>%
pivot_wider(names_from = sample_readname, values_from = count, values_fill = 0)
print(stable.reads.matrix)
m <- as.matrix(sapply(stable.reads.matrix[, 2:ncol(stable.reads.matrix)], as.numeric))
rownames(m) <- stable.reads.matrix$monomer_type
#m <- as.matrix(sapply(stable.reads.table_filtered[, 2:ncol(stable.reads.table_filtered)], as.numeric))
#rownames(m) <- stable.reads.table_filtered$monomer_type
m[is.na(m)] <- 0
m_g0 <- m[, colSums(m) >= 10]
sample_names <- sapply(strsplit(colnames(m_g0), "_"), `[`, 1)
unique_samples <- unique(sample_names)
sample_colors <- setNames(rainbow(length(unique_samples)), unique_samples)
ha <- columnAnnotation(
Sample = sample_names,
col = list(Sample = sample_colors)
)
col_fun <- colorRamp2(c(min(m_g0), max(m_g0) / 2, max(m_g0)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
m_g0 <- m_g0[order(rowSums(m_g0), decreasing = TRUE), ]
fig1 <- Heatmap(
m_g0,
cluster_columns = TRUE,
cluster_rows = TRUE,
clustering_distance_columns = "pearson", # pretty good
clustering_distance_rows = "pearson",
# clustering_distance_columns = "spearman", # bad
# clustering_distance_columns = "kendall", # bad
#clustering_distance_columns = "euclidean", # default
# clustering_distance_columns = "maximum", # bad
# clustering_distance_columns = "manhattan",# pretty good
# clustering_distance_columns = "canberra", # pretty good
# clustering_distance_columns = "binary", # pretty good
# clustering_distance_columns = "minkowski",# pretty good
row_names_side = "left",
show_column_names = FALSE,
show_row_dend = FALSE,
show_column_dend = TRUE,
column_names_side = "top",
row_dend_side = "left",
col = col_fun,
row_names_gp = gpar(fontsize = 15),
column_names_gp = gpar(fontsize = 2),
heatmap_legend_param = list(title = "Number of alignments"),
top_annotation = ha
)
fig1 <- draw(fig1, annotation_legend_side = "right")
pdf("/Users/pavel/Desktop/monomer_types_heatmap.pdf", width = 15, height = 7)
draw(fig1)
dev.off()
null device
1
stable.reads.table_long = stable.reads.matrix %>%
pivot_longer(cols = -monomer_type, names_to = "sample", values_to = "count")
stable.reads.table_long = stable.reads.table_long %>%
group_by(sample) %>%
mutate(pct = count / sum(count)) %>%
ungroup()
dominant = stable.reads.table_long %>%
group_by(sample) %>%
filter(pct == max(pct)) %>%
filter(pct > 0.95)
stable.reads.table_filtered = stable.reads.matrix %>%
filter(monomer_type %in% dominant$monomer_type) %>%
select(monomer_type, all_of(dominant$sample))
stable.reads.table_filtered
sum(select(stable.reads.table_filtered, where(is.numeric)))
[1] 219537
col_names = colnames(stable.reads.table_filtered)
output = tibble(
sample = col_names
)
output = output[-1,]
stable.dinucl.monomer.reads = stable.reads.f %>%
filter(sample_readname %in% output$sample)
stable.dinucl.monomer.reads.f = stable.dinucl.monomer.reads %>%
group_by(sample_readname) %>%
mutate(freq = ave(seq_along(monomer_type), monomer_type, FUN = length)) %>%
group_by(sample_readname, monomer_type) %>%
mutate(monomer_type_count = n()) %>%
ungroup() %>%
group_by(sample_readname) %>%
filter(monomer_type_count == max(monomer_type_count)) %>%
select(-freq, -monomer_type_count) %>%
ungroup()
stable.dinucl.monomer.reads %>%
select(sample_readname) %>%
arrange() %>%
unique() %>%
summarise(count = n())
stable.dinucl.monomer.reads.f <- stable.dinucl.monomer.reads.f %>%
mutate(strand_combo = paste0(monomer_strand, read_strand))
valid_sample_readnames <- stable.dinucl.monomer.reads.f %>%
group_by(sample_readname) %>%
summarise(
n_total = n(),
n_same = sum(strand_combo %in% c("++", "--")),
n_opposite = sum(strand_combo %in% c("+-", "-+")),
prop_same = n_same / n_total,
prop_opposite = n_opposite / n_total
) %>%
filter(prop_same >= 0.95 | prop_opposite >= 0.95) %>%
pull(sample_readname)
stable.dinucl.monomer.reads.strand <- stable.dinucl.monomer.reads.f %>%
filter(sample_readname %in% valid_sample_readnames)
orientation_info <- stable.dinucl.monomer.reads.strand %>%
group_by(sample_readname) %>%
summarise(
prop_same = mean(strand_combo %in% c("++", "--")),
prop_opposite = mean(strand_combo %in% c("+-", "-+")),
dominant_orientation = if_else(prop_same >= prop_opposite, "same", "opposite")
)
stable.dinucl.monomer.reads.strand <- stable.dinucl.monomer.reads.strand %>%
left_join(orientation_info, by = "sample_readname") %>%
filter(
(dominant_orientation == "same" & strand_combo %in% c("++", "--")) |
(dominant_orientation == "opposite" & strand_combo %in% c("+-", "-+"))
)
stable.dinucl.monomer.reads.strand.matrix = stable.dinucl.monomer.reads.strand %>%
group_by(strand_combo, sample_readname) %>%
summarise(count = n(), .groups = "drop") %>%
pivot_wider(names_from = sample_readname, values_from = count, values_fill = 0)
print(stable.dinucl.monomer.reads.strand.matrix)
m <- as.matrix(sapply(stable.dinucl.monomer.reads.strand.matrix[, 2:ncol(stable.dinucl.monomer.reads.strand.matrix)], as.numeric))
rownames(m) <- stable.dinucl.monomer.reads.strand.matrix$strand_combo
#m <- as.matrix(sapply(stable.reads.table_filtered[, 2:ncol(stable.reads.table_filtered)], as.numeric))
#rownames(m) <- stable.reads.table_filtered$strand_combo
m[is.na(m)] <- 0
m_g0 <- m[, colSums(m) >= 10]
sample_names <- sapply(strsplit(colnames(m_g0), "_"), `[`, 1)
unique_samples <- unique(sample_names)
sample_colors <- setNames(rainbow(length(unique_samples)), unique_samples)
ha <- columnAnnotation(
Sample = sample_names,
col = list(Sample = sample_colors)
)
col_fun <- colorRamp2(c(min(m_g0), max(m_g0) / 2, max(m_g0)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
m_g0 <- m_g0[order(rowSums(m_g0), decreasing = TRUE), ]
fig1 <- Heatmap(
m_g0,
cluster_columns = TRUE,
cluster_rows = TRUE,
clustering_distance_columns = "pearson", # pretty good
clustering_distance_rows = "pearson",
# clustering_distance_columns = "spearman", # bad
# clustering_distance_columns = "kendall", # bad
#clustering_distance_columns = "euclidean", # default
# clustering_distance_columns = "maximum", # bad
# clustering_distance_columns = "manhattan",# pretty good
# clustering_distance_columns = "canberra", # pretty good
# clustering_distance_columns = "binary", # pretty good
# clustering_distance_columns = "minkowski",# pretty good
row_names_side = "left",
show_column_names = FALSE,
show_row_dend = FALSE,
show_column_dend = TRUE,
column_names_side = "top",
row_dend_side = "left",
col = col_fun,
row_names_gp = gpar(fontsize = 25),
column_names_gp = gpar(fontsize = 2),
heatmap_legend_param = list(title = "Number of alignments"),
top_annotation = ha
)
fig1 <- draw(fig1, annotation_legend_side = "right")
sum(m)
[1] 219328
pdf("/Users/pavel/Desktop/strands_alignments_heatmap.pdf", width = 15, height = 7)
draw(fig1)
dev.off()
null device
1
stable.dinucl.monomer.reads.strand.f = stable.dinucl.monomer.reads.strand %>%
group_by(sample_readname) %>%
mutate(freq = ave(seq_along(dominant_orientation), dominant_orientation, FUN = length)) %>%
group_by(sample_readname, dominant_orientation) %>%
mutate(dominant_orientation_count = n()) %>%
ungroup() %>%
group_by(sample_readname) %>%
filter(dominant_orientation_count == max(dominant_orientation_count)) %>%
select(-freq, -dominant_orientation_count) %>%
ungroup()
stable.dinucl.monomer.reads.strand.f %>%
select(sample_readname) %>%
arrange() %>%
unique() %>%
summarise(count = n())
stable.dinucl.monomer.reads.strand.f = stable.dinucl.monomer.reads.strand.f %>%
mutate(monomer_length = monomer_end - monomer_start) %>%
mutate(distance_to_start = initiator_start - monomer_start) %>%
mutate(distance_to_end = monomer_end - initiator_start) %>%
mutate(
relative_distance = case_when(
monomer_strand == "+" ~ initiator_start - monomer_start,
monomer_strand == "-" ~ monomer_end - initiator_end
)) %>%
mutate(proportion_distance_to_start = distance_to_start / monomer_length) %>%
mutate(proportion_distance_to_end = distance_to_end / monomer_length) %>%
mutate(proportion_relative_distance = relative_distance / monomer_length)
# m+i+ initiator_start - monomer_start
# m-i- monomer_end - initiator_start
# m+i- initiator_start - monomer_start
# m-i+ monomer_end - initiator_start
ggplot(stable.dinucl.monomer.reads.strand.f, aes(x = monomer_type, y = monomer_length, fill = monomer_type, color = monomer_type)) +
#geom_violin(trim = FALSE) +
geom_jitter(width = 0.2, alpha = 0.1) +
scale_y_continuous(breaks = seq(0, 200, 1), limits = c(155, 175)) +
labs(x = "Monomer Type",
y = "Length") +
theme_classic() +
theme(legend.position = "none")
Warning: Removed 252 rows containing missing values or values outside the scale range
(`geom_point()`).
#stable.dinucl.monomer.reads.strand.f <- stable.dinucl.monomer.reads.strand.f %>%
# mutate(type = as.character(unlist(type)))
#length_heatmap_data_filt = stable.dinucl.monomer.reads.strand.f %>%
length_heatmap_data_filt = filtered_stable.dinucl.monomer.reads %>%
filter(monomer_length >= 160, monomer_length <= 175) %>%
count(monomer_length, monomer_type) %>%
pivot_wider(names_from = monomer_type, values_from = n, values_fill = list(n = 0)) %>%
arrange(desc(monomer_length)) %>%
column_to_rownames("monomer_length")
length_matrix_filt <- as.matrix(length_heatmap_data_filt)
length_matrix_filt <- length_matrix_filt[, sort(colnames(length_matrix_filt))]
lengths <- as.integer(rownames(length_matrix_filt))
row_labels_to_show <- ifelse(lengths %% 1 == 0, as.character(lengths), "")
length_matrix_filt <- sweep(length_matrix_filt, 2, colSums(length_matrix_filt), FUN = "/")
col_fun = colorRamp2(c(0, 0.5, 1), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
#col_fun = colorRamp2(c(min(length_matrix_filt), max(length_matrix_filt) / 5, max(length_matrix_filt)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
fig3 = Heatmap(length_matrix_filt,
name = "Column-wise proportion",
col = col_fun,
cluster_rows = FALSE,
cluster_columns = FALSE,
row_title = "Length",
column_title = "Monomer Type",
row_names_side = "left",
row_labels = row_labels_to_show,
column_names_rot = 0)
draw(fig3)
pdf("/Users/pavel/Desktop/monomer_lengths_mapped_heatmap.pdf", width = 10, height = 7)
draw(fig3)
dev.off()
null device
1
ggplot(stable.dinucl.monomer.reads.strand.f,
aes(#x = proportion_distance_to_start,
#x = proportion_distance_to_end,
x = proportion_relative_distance,
color = "black")) +
#geom_point() +
geom_bar() +
labs(x = "Proportion of distance from the start of the monomer",
y = "Count") +
theme_classic()
strand_matrix = stable.dinucl.monomer.reads.strand.f %>%
count(monomer_strand, read_strand) %>%
complete(monomer_strand = c("+", "-"), read_strand = c("+", "-"), fill = list(n = 0))
ggplot(strand_matrix, aes(x = read_strand, y = monomer_strand, fill = n)) +
geom_tile(color = "white") +
geom_text(aes(label = n), size = 6) +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(x = "Read strand", y = "Monomer strand", fill = "Count") +
theme_classic() +
theme(
axis.text.x = element_text(size = 25),
axis.text.y = element_text(size = 25)
)
write_tsv(stable.dinucl.monomer.reads, "/Users/pavel/Desktop/stable.dinucl.monomer.reads.tsv", col_names = TRUE)
proportion_distance_to_start proportion_distance_to_end proportion_relative_distance
# Более наглядно просто в относительной длине
filtered_stable.dinucl.monomer.alignments = stable.dinucl.monomer.reads.strand.f %>%
group_by(monomer_type, sample_readname, monomer_length) %>%
mutate(mode_dist = relative_distance[which.max(tabulate(match(relative_distance, relative_distance)))],
mode_dist_freq = mean(relative_distance == mode_dist)) %>%
filter(mode_dist_freq >= 0.95) %>%
ungroup()
filtered_stable.dinucl.monomer.alignments
filtered_stable.dinucl.monomer.alignments %>%
select(sample_readname) %>%
arrange() %>%
unique() %>%
summarise(count = n())
filtered_stable.dinucl.monomer.alignments.f = filtered_stable.dinucl.monomer.alignments %>%
group_by(sample_readname) %>%
mutate(freq = ave(seq_along(mode_dist), mode_dist, FUN = length)) %>%
group_by(sample_readname, mode_dist) %>%
mutate(mode_dist_count = n()) %>%
ungroup() %>%
group_by(sample_readname) %>%
filter(mode_dist_count == max(mode_dist_count)) %>%
select(-freq, -mode_dist_count) %>%
ungroup()
filtered_stable.dinucl.monomer.reads = filtered_stable.dinucl.monomer.alignments.f %>%
select(-c(monomer_chr,
monomer_start,
monomer_end,
#monomer_strand,
initiator_chr,
initiator_start,
initiator_end,
#read_strand,
strand_combo,
prop_same,
prop_opposite,
distance_to_start,
distance_to_end,
relative_distance,
proportion_distance_to_start,
proportion_distance_to_end,
proportion_relative_distance,
mode_dist_freq)) %>%
arrange(monomer_type, sample_readname, monomer_length) %>%
unique() %>%
mutate(relative_distance = mode_dist / monomer_length)
filtered_stable.dinucl.monomer.reads
monomer_counts <- filtered_stable.dinucl.monomer.reads %>%
group_by(monomer_type, sample) %>%
summarise(unique_reads = n_distinct(read_name)) %>%
arrange(desc(unique_reads)) %>%
ungroup() %>%
group_by(monomer_type) %>%
mutate(unique_reads_by_type = sum(unique_reads)) %>%
ungroup()
`summarise()` has grouped output by 'monomer_type'. You can override using the
`.groups` argument.
ggplot(monomer_counts, aes(x = reorder(monomer_type, -unique_reads_by_type), y = unique_reads, color = sample, fill = sample)) +
geom_bar(stat = "identity") +
geom_hline(yintercept = 43.6, linetype = 2) +
labs(
x = "Monomer Type",
y = "Unique Reads"
) +
theme_classic() #+
#ggsave(filename = paste0("/Users/pavel/Desktop/","monomer_read_count.pdf"),width = 7,height=5)
#theme(legend.position = "None")
872*0.05
[1] 43.6
filtered_stable.dinucl.monomer.reads %>%
select(sample_readname) %>%
unique() %>%
summarise(count = n())
# не даем в тексте
ggplot(filtered_stable.dinucl.monomer.reads, aes(x = relative_distance, color = sample, fill = sample)) +
#geom_point() +
#geom_bar(width=0.1, position = "stack") +
geom_histogram(binwidth=0.01, position = "stack") +
labs(x = "Distance from end of the monomer",
y = "Unique alignments") +
scale_x_continuous(breaks = seq(0, 1, 0.05)) +
theme_classic()
most_frequent_lengths = filtered_stable.dinucl.monomer.reads %>%
group_by(monomer_type, monomer_length) %>%
summarise(count = n(), .groups = "drop") %>%
group_by(monomer_type) %>%
mutate(rank = dense_rank(desc(count))) %>%
filter(rank <= 2) %>%
arrange(monomer_type, rank)
most_frequent_lengths
filtered_stable.dinucl.monomer.reads.DJ = filtered_stable.dinucl.monomer.reads %>%
filter(monomer_type %in% c("D1", "D2", "J1", "J2", "W1")) %>%
filter(
(monomer_type == "D1" & monomer_length >= 161 & monomer_length <= 171) |
(monomer_type == "D2" & monomer_length >= 164 & monomer_length <= 171) |
(monomer_type == "J1" & monomer_length >= 163 & monomer_length <= 172) |
(monomer_type == "J2" & monomer_length >= 164 & monomer_length <= 170) |
(monomer_type == "W1" & monomer_length >= 135 & monomer_length <= 171)
)
filtered_stable.dinucl.monomer.reads.DJ.exact_length = filtered_stable.dinucl.monomer.reads.DJ %>%
filter(
(monomer_type == "D1" & (monomer_length == 166 | monomer_length == 167)) |
(monomer_type == "D2" & (monomer_length == 171 | monomer_length == 170)) |
(monomer_type == "J1" & (monomer_length == 169 | monomer_length == 170)) |
(monomer_type == "J2" & (monomer_length == 169 | monomer_length == 167)) |
(monomer_type == "W1" & (monomer_length == 164 | monomer_length == 163))
) %>%
mutate(
type_length = paste(monomer_type, monomer_length, sep = "-")
)
filtered_stable.dinucl.monomer.reads.DJ$dominant_orientation <- factor(
filtered_stable.dinucl.monomer.reads.DJ$dominant_orientation,
levels = c("same", "opposite")
)
orientation_labels <- c(
"same" = "Same direction",
"opposite" = "Opposite direction"
)
ggplot(filtered_stable.dinucl.monomer.reads.DJ, aes(x = relative_distance, color = sample, fill = sample)) +
geom_histogram(binwidth = 0.01, position = "stack") +
geom_hline(yintercept = 10, linetype = 2) +
labs(x = "Proportion of distance to the 5'-end of the monomer", y = "Reads") +
scale_x_continuous(breaks = seq(0, 1, 0.1)) +
theme_classic() +
theme(
text = element_text(size = 20),
axis.text = element_text(size = 15),
axis.title = element_text(size = 20),
strip.text = element_text(size = 20),
strip.text.y = element_text(size = 20, angle = 0),
legend.text = element_text(size = 20),
legend.title = element_text(size = 20)
) +
facet_grid(rows = vars(monomer_type), cols = vars(dominant_orientation),
scales = "free_y",
labeller = labeller(dominant_orientation = orientation_labels)
) #+
ggsave(filename = paste0("/Users/pavel/Desktop/","proportion_relative_distance.pdf"),width = 12,height=9)
filtered_stable.dinucl.monomer.reads.DJ.exact_length$dominant_orientation <- factor(
filtered_stable.dinucl.monomer.reads.DJ.exact_length$dominant_orientation,
levels = c("same", "opposite")
)
orientation_labels <- c(
"same" = "Same direction",
"opposite" = "Opposite direction"
)
ggplot(filtered_stable.dinucl.monomer.reads.DJ.exact_length, aes(x = mode_dist, color = sample, fill = sample)) +
geom_histogram(binwidth = 0.01, position = "stack") +
geom_hline(yintercept = 10, linetype = 2) +
# geom_text(data = annotation_df, aes(x = x, y = y, label = label), inherit.aes = FALSE) +
labs(x = "Distance to the 5'-end of the monomer", y = "Reads") +
scale_x_continuous(breaks = seq(0, 171, 10)) +
theme_classic() +
theme(
text = element_text(size = 20),
axis.text = element_text(size = 10),
axis.title = element_text(size = 20),
strip.text = element_text(size = 20),
strip.text.y = element_text(size = 20, angle = 0),
legend.text = element_text(size = 20),
legend.title = element_text(size = 20)
) +
facet_grid(rows = vars(type_length), cols = vars(dominant_orientation),
scales = "free_y",
labeller = labeller(dominant_orientation = orientation_labels)
)
ggsave(filename = paste0("/Users/pavel/Desktop/","proportion_relative_distance_split.pdf"),plot = last_plot(),width = 15,height=9)
filtered_stable.dinucl.monomer.reads.DJ.exact_length.f = filtered_stable.dinucl.monomer.reads.DJ.exact_length %>%
filter(type_length %in% c("D1-167", "J2-169", "D1-166") & dominant_orientation %in% c("same", "opposite") | type_length %in% c("J1-169", "W1-163", "W1-164") & dominant_orientation %in% c("same", "opposite")) %>%
filter()
annotation_df = data.frame(
type_length = c("D1-167", "D1-167", "D1-167", "D1-167", "D1-167", "J2-169", "D1-166", "D1-166", "J1-169", "W1-163", "W1-164"),
dominant_orientation = c("same", "same", "same", "same", "same", "same", "same", "same", "opposite", "opposite", "opposite"),
x = c(160, 159, 158, 157, 156, 118, 33, 49, 135, 50, 50),
y = c(350, 140, 120, 100, 80, 45, 13, 13, 12, 11, 11),
label = c("TT", "TC", "CT", "CA", "AC", "CA", "TA", "CG", "TG", "TT", "TT")
)
filtered_stable.dinucl.monomer.reads.DJ.exact_length.f$dominant_orientation = factor(
filtered_stable.dinucl.monomer.reads.DJ.exact_length.f$dominant_orientation,
levels = c("same", "opposite")
)
annotation_df$dominant_orientation = factor(
annotation_df$dominant_orientation,
levels = c("same", "opposite")
)
orientation_labels <- c(
"same" = "Same direction",
"opposite" = "Opposite direction"
)
ggplot(filtered_stable.dinucl.monomer.reads.DJ.exact_length.f, aes(x = mode_dist, color = sample, fill = sample)) +
geom_histogram(binwidth = 0.01, position = "stack") +
geom_text(data = annotation_df, aes(x = x, y = y, label = label), inherit.aes = FALSE) +
labs(x = "Relative distance from the start of the monomer", y = "Reads") +
scale_x_continuous(breaks = seq(0, 171, 10)) +
theme_classic() +
facet_grid(rows = vars(type_length), cols = vars(dominant_orientation),
scales = "free_y",
labeller = labeller(dominant_orientation = orientation_labels)
) +
theme(strip.placement = "outside",
#axis.text.x = element_text(),
axis.ticks.x = element_line(),
text = element_text(size = 20),
axis.text = element_text(size = 10),
axis.title = element_text(size = 20),
strip.text = element_text(size = 20),
strip.text.y = element_text(size = 20, angle = 0),
legend.text = element_text(size = 20),
legend.title = element_text(size = 20)
) #+
ggsave(filename = paste0("/Users/pavel/Desktop/","proportion_relative_distance_sele.pdf"),width = 12,height=9)
filtered_stable.dinucl.monomer.reads.DJ.exact_length.f = filtered_stable.dinucl.monomer.reads.DJ.exact_length %>%
filter(type_length %in% c("D1-166", "J2-169") & dominant_orientation %in% c("same", "opposite") | type_length %in% c("J1-169", "W1-163", "W1-164") & dominant_orientation %in% c("same", "opposite")) %>%
filter()
annotation_df = data.frame(
type_length = c("J2-169", "J2-169", "J2-169", "J2-169", "J2-169","D1-166", "D1-166","J1-169", "W1-163", "W1-164"),
dominant_orientation = c("same", "same", "same", "same","same", "same", "same", "opposite", "opposite", "opposite"),
x = c(90, 68, 149, 110, 112, 27, 56, 130, 47, 47),
y = c(15, 17, 15, 20, 48, 11, 11, 12, 13, 13),
label = c("TG", "CA", "CA", "CA", "CA", "TA", "CG","TG", "TT", "TT")
)
filtered_stable.dinucl.monomer.reads.DJ.exact_length.f$dominant_orientation = factor(
filtered_stable.dinucl.monomer.reads.DJ.exact_length.f$dominant_orientation,
levels = c("same", "opposite")
)
annotation_df$dominant_orientation = factor(
annotation_df$dominant_orientation,
levels = c("same", "opposite")
)
orientation_labels <- c(
"same" = "Same direction",
"opposite" = "Opposite direction"
)
ggplot(filtered_stable.dinucl.monomer.reads.DJ.exact_length.f, aes(x = mode_dist, color = sample, fill = sample)) +
geom_histogram(binwidth = 0.01, position = "stack") +
geom_text(data = annotation_df, aes(x = x, y = y, label = label),
inherit.aes = FALSE,
size = 5) +
labs(x = "Distance to the 5'-end of the monomer", y = "Reads") +
scale_x_continuous(breaks = seq(0, 170, 10)) +
theme_classic() +
facet_grid(rows = vars(type_length), cols = vars(dominant_orientation),
scales = "free_y",
labeller = labeller(dominant_orientation = orientation_labels)
) +
theme(strip.placement = "outside",
#axis.text.x = element_text(),
axis.ticks.x = element_line(),
text = element_text(size = 20),
axis.text = element_text(size = 10),
axis.title = element_text(size = 20),
strip.text = element_text(size = 20),
strip.text.y = element_text(size = 20, angle = 0),
legend.text = element_text(size = 20),
legend.title = element_text(size = 20)
) #+
ggsave(filename = paste0("/Users/pavel/Desktop/","proportion_relative_distance_sele.pdf"),width = 15,height=9)
# Точный тест Фишера или хи квадрат
# 4 теста с поправкой
monomer_strands = filtered_stable.dinucl.monomer.reads %>%
select(monomer_type, monomer_strand, read_strand) %>%
filter(monomer_type %in% c("D1", "D2", "J1", "J2", "W1"))
strand_matrix_all <- monomer_strands %>%
count(monomer_type, monomer_strand, read_strand) %>%
complete(
monomer_type,
monomer_strand = c("+", "-"),
read_strand = c("+", "-"),
fill = list(n = 0)
)
ggplot(strand_matrix_all, aes(x = read_strand, y = monomer_strand, fill = n)) +
geom_tile(color = "white") +
geom_text(aes(label = n), size = 6) +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(x = "Read strand", y = "Monomer strand", fill = "Read Count") +
theme_classic() +
facet_wrap(~ monomer_type) +
theme(
strip.text = element_text(size = 25),
axis.text.x = element_text(size = 25),
axis.text.y = element_text(size = 25),
axis.title = element_text(size = 25),
legend.title = element_text(size = 25),
legend.text = element_text(size = 25)
) #+
ggsave(filename = paste0("/Users/pavel/Desktop/","strand_statistics.pdf"),width = 14,height=9)
filtered_stable.dinucl.monomer.reads %>%
group_by(monomer_type, monomer_strand) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(monomer_type) %>%
filter(monomer_type %in% c("D1", "D2", "J1", "J2", "W1")) %>%
mutate(monomer_strand = factor(monomer_strand, levels = c("+", "-"))) %>%
ggplot(aes(x = monomer_type, y = count, fill = monomer_strand)) +
geom_bar(stat = "identity", position = "dodge") +
theme_classic(base_size = 20) +
labs(x = "Monomer type",
y = "Count",
fill = "Strand")
`summarise()` has grouped output by 'monomer_type'. You can override using the
`.groups` argument.
ggsave(filename = paste0("/Users/pavel/Desktop/","monomer_strand_count_mapped.pdf"),width = 7,height=5)
fisher_results = strand_matrix_all %>%
group_by(monomer_type) %>%
summarise(
minus_minus = n[monomer_strand == "-" & read_strand == "-"],
minus_plus = n[monomer_strand == "-" & read_strand == "+"],
plus_minus = n[monomer_strand == "+" & read_strand == "-"],
plus_plus = n[monomer_strand == "+" & read_strand == "+"],
.groups = "drop"
) %>%
rowwise() %>%
mutate(
fisher_p = fisher.test(matrix(c(minus_minus, minus_plus, plus_minus, plus_plus), nrow = 2))$p.value
)
fisher_results = fisher_results %>%
mutate(
p_adj_bonf = p.adjust(fisher_p, method = "bonferroni"),
#p_adj_holm = p.adjust(fisher_p, method = "holm"),
#p_adj_fdr = p.adjust(fisher_p, method = "BH")
)
print(fisher_results)
# fisher_results %>%
# select(monomer_type, fisher_p, p_adj_bonf, p_adj_holm, p_adj_fdr)
type_initiator_heatmap_data <- filtered_stable.dinucl.monomer.reads %>%
count(monomer_type, initiator_dinucleotide) %>%
group_by(monomer_type) %>%
mutate(prop = n / sum(n)) %>%
select(-n) %>%
ungroup()
type_initiator_heatmap_matrix <- type_initiator_heatmap_data %>%
pivot_wider(names_from = initiator_dinucleotide, values_from = prop, values_fill = 0) %>%
column_to_rownames("monomer_type") %>%
as.matrix()
col_fun <- colorRamp2(c(min(type_initiator_heatmap_matrix), max(type_initiator_heatmap_matrix) / 2, max(type_initiator_heatmap_matrix)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
fig4 = Heatmap(
type_initiator_heatmap_matrix,
name = "Proportion",
col = col_fun,
cluster_rows = FALSE,
cluster_columns = FALSE,
column_names_side = "top",
row_names_side = "left",
column_names_rot = 0,
column_names_centered = TRUE,
row_names_gp = gpar(fontsize = 12),
column_names_gp = gpar(fontsize = 12),
heatmap_legend_param = list(
title = "Proportion of read counts by rows",
at = seq(0, 1, 0.2),
labels = scales::percent(seq(0, 1, 0.2))
)
)
fig4
pdf("/Users/pavel/Desktop/monomer_dinucleotide_heatmap.pdf", width = 8, height = 7)
fig4
dev.off()
null device
1
hmdf = as.data.frame(type_initiator_heatmap_matrix)
hmdf$sum.of.row = rowSums(hmdf)
# Не идет в текст
filtered_stable.dinucl.monomer.reads.matrix = filtered_stable.dinucl.monomer.reads %>%
group_by(initiator_dinucleotide, sample_readname) %>%
summarise(count = n(), .groups = "drop") %>%
pivot_wider(names_from = sample_readname, values_from = count, values_fill = 0)
m = as.matrix(sapply(filtered_stable.dinucl.monomer.reads.matrix[, 2:ncol(filtered_stable.dinucl.monomer.reads.matrix)], as.numeric))
rownames(m) = filtered_stable.dinucl.monomer.reads.matrix$initiator_dinucleotide
m[is.na(m)] = 0
sample_names = sapply(strsplit(colnames(m), "_"), `[`, 1)
unique_samples = unique(sample_names)
sample_colors = setNames(rainbow(length(unique_samples)), unique_samples)
ha = columnAnnotation(
Sample = sample_names,
col = list(Sample = sample_colors)
)
col_fun <- colorRamp2(c(min(m), max(m) / 2, max(m)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
fig1 <- Heatmap(
m,
cluster_columns = TRUE,
cluster_rows = TRUE,
clustering_distance_columns = "pearson", # pretty good
clustering_distance_rows = "pearson",
#clustering_distance_columns = "euclidean", # default
row_names_side = "left",
show_column_names = FALSE,
show_row_dend = FALSE,
show_column_dend = TRUE,
column_names_side = "top",
row_dend_side = "left",
col = col_fun,
row_names_gp = gpar(fontsize = 15),
column_names_gp = gpar(fontsize = 2),
heatmap_legend_param = list(title = "Number of reads"),
top_annotation = ha
)
fig1 <- draw(fig1, annotation_legend_side = "right")
filtered_stable.dinucl.monomer.reads.matrix = filtered_stable.dinucl.monomer.reads %>%
group_by(monomer_type, sample_readname) %>%
summarise(count = n(), .groups = "drop") %>%
pivot_wider(names_from = sample_readname, values_from = count, values_fill = 0)
m = as.matrix(sapply(filtered_stable.dinucl.monomer.reads.matrix[, 2:ncol(filtered_stable.dinucl.monomer.reads.matrix)], as.numeric))
rownames(m) = filtered_stable.dinucl.monomer.reads.matrix$monomer_type
m[is.na(m)] = 0
sample_names = sapply(strsplit(colnames(m), "_"), `[`, 1)
unique_samples = unique(sample_names)
sample_colors = setNames(rainbow(length(unique_samples)), unique_samples)
ha = columnAnnotation(
Sample = sample_names,
col = list(Sample = sample_colors)
)
col_fun <- colorRamp2(c(min(m), max(m) / 2, max(m)), c("#0d0887ff", "#b12a90ff", "#f0f921ff"))
fig1 <- Heatmap(
m,
cluster_columns = TRUE,
cluster_rows = TRUE,
clustering_distance_columns = "pearson", # pretty good
clustering_distance_rows = "pearson",
#clustering_distance_columns = "euclidean", # default
row_names_side = "left",
show_column_names = FALSE,
show_row_dend = FALSE,
show_column_dend = TRUE,
column_names_side = "top",
row_dend_side = "left",
col = col_fun,
row_names_gp = gpar(fontsize = 15),
column_names_gp = gpar(fontsize = 2),
heatmap_legend_param = list(title = "Number of reads"),
top_annotation = ha
)
fig1 <- draw(fig1, annotation_legend_side = "right")
strand_matrix = filtered_stable.dinucl.monomer.reads %>%
count(monomer_strand, read_strand) %>%
complete(monomer_strand = c("+", "-"), read_strand = c("+", "-"), fill = list(n = 0))
ggplot(strand_matrix, aes(x = read_strand, y = monomer_strand, fill = n)) +
geom_tile(color = "white") +
geom_text(aes(label = n), size = 6) +
scale_fill_gradient(low = "white", high = "steelblue") +
labs(x = "Read strand", y = "Monomer strand", fill = "Count") +
theme_classic() +
theme(
axis.text.x = element_text(size = 20),
axis.text.y = element_text(size = 20)
)
library(CAGEr)
library(BSgenome)
library(BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0)
library(viridis)
library(ggplot2)
library(reshape2)
library(dplyr)
library(data.table)
library(tidytext)
library(GenomicRanges)
library(sparseMatrixStats)
library(DelayedMatrixStats)
star_TSS = readRDS("/Users/pavel/Desktop/SoftclipG_CAGEexp_T2T_CHM13v2.0_annotated.RDS")
#genome(star_TSS) = "BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0"
genome = BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0
tagClustersGR(star_TSS) = tag_clusters
Error in tagClustersGR(star_TSS) = tag_clusters :
could not find function "tagClustersGR<-"
# Save BW tracks
library(CAGEr)
library(rtracklayer)
library(GenomicRanges)
star_TSS = readRDS("/Users/pavel/Desktop/SoftclipG_CAGEexp_T2T_CHM13v2.0_annotated.RDS")
star_TSS = normalizeTagCount(star_TSS, method = "powerLaw", T = 10^6)
Normalizing tag count...
Warning in dim(assays) :
The dim() method for DataFrameList objects is deprecated. Please use
dims() on these objects instead.
Warning in nrow(x) :
The nrow() method for DataFrameList objects is deprecated. Please use
nrows() on these objects instead.
Warning in ncol(x) :
The ncol() method for DataFrameList objects is deprecated. Please use
ncols() on these objects instead.
Warning in dim(assays) :
The dim() method for DataFrameList objects is deprecated. Please use
dims() on these objects instead.
Warning in nrow(x) :
The nrow() method for DataFrameList objects is deprecated. Please use
nrows() on these objects instead.
Warning in ncol(x) :
The ncol() method for DataFrameList objects is deprecated. Please use
ncols() on these objects instead.
star_TSS = filterLowExpCTSS(star_TSS, thresholdIsTpm = TRUE, nrPassThreshold = 1, threshold = 1)
samples = unname(sampleLabels(star_TSS))
tagcount_exp = experiments(star_TSS)[[1]]
gr_ctss = rowRanges(tagcount_exp)
ctss_matrix = assays(tagcount_exp)[[1]]
genome_fixed = BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0
seqlevels(genome_fixed) = paste0("chr", seqlevels(genome_fixed))
seqlevels(genome_fixed)[seqlevels(genome_fixed) == "chrMT"] = "chrM"
genome_lengths = seqlengths(genome_fixed)
for (sample in samples) {
message("Processing sample: ", sample)
gr = GRanges(
seqnames = seqnames(gr_ctss),
ranges = IRanges(start = start(gr_ctss), width = 1),
strand = strand(gr_ctss),
score = ctss_matrix[, sample]
)
seqlengths(gr) = genome_lengths[names(seqlengths(gr))]
gr_plus = gr[strand(gr) == "+"]
gr_minus = gr[strand(gr) == "-"]
export(gr_plus, format = "BigWig", con = paste0("/Users/pavel/Desktop/star_normalized_bws/", sample, "_F.bw"))
export(gr_minus, format = "BigWig", con = paste0("/Users/pavel/Desktop/star_normalized_bws/", sample, "_R.bw"))
}
Processing sample: ArticularChondrocyteKneeJoint
Processing sample: BCell
Processing sample: BronchialSmoothMuscleCells
Processing sample: CD14PositiveMonocyte
Processing sample: ErythroidProgenitor
Processing sample: FANTOM6DermalBloodVascularEndothelialCells
Processing sample: FANTOM6DermalFibroblasts
Processing sample: FANTOM6DermalLymphaticVascularEndothelialCells
Processing sample: FibroblastDermisAniso1
Processing sample: FibroblastDermisAniso2
Processing sample: GM12878
Processing sample: H9hESC
Processing sample: HeLaS3
Processing sample: HematopoieticProgenitor
Processing sample: HepG2
Processing sample: K562
Processing sample: KeratinocyteFemale
Processing sample: KeratinocyteProgenitor
Processing sample: MCF7
Processing sample: MyeloidProgenitor
Processing sample: OsteoblastAniso1
Processing sample: OsteoblastAniso2
Processing sample: SkeletalMuscleSatelliteAniso1
Processing sample: SkeletalMuscleSatelliteAniso2
Processing sample: UmbilicalVeinCytosolicFraction
Processing sample: VillousMesenchymeAniso1
Processing sample: VillousMesenchymeAniso2
seqlengths(genome)
genome[["1"]]
genome[["chr1"]]
CTSStagCountSE(star_TSS)
CTSScoordinatesGR(star_TSS)
CTSStagCountDF(star_TSS)
CTSStagCountGR(star_TSS, 1)
# Check if anything is in the signal matrix
dim(CTSStagCountSE(star_TSS))
# Check total number of CTSS positions
length(rowRanges(CTSStagCountSE(star_TSS)))
# Check total counts per sample
star_TSS$librarySizes
# View the first few ranges
head(rowRanges(CTSStagCountSE(star_TSS)))
# Peek at one sample’s counts
assay(CTSStagCountSE(star_TSS))[,1][1:10]
pdf("/Users/pavel/Desktop/plotAnnot.pdf", width = 9, height = 5)
plotAnnot(star_TSS, "counts")
dev.off()
# first version
library(BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0)
genome = BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0
tmp = as.data.frame(CTSScoordinatesGR(object = star_TSS), row.names = NULL)
tmp$score = rowSums(as.data.frame(CTSSnormalizedTpmDF(object = star_TSS)))
tmp = tmp[which(tmp$filteredCTSSidx == "FALSE"),]
seqlevels(genome) = paste0("chr", seqlevels(genome))
tmp$seqnames = as.character(tmp$seqnames)
tmp$seqnames[tmp$seqnames == "chrM"] = "chrMT"
tmp = GRanges(
seqnames = tmp$seqnames,
ranges = IRanges(start = tmp$start, end = tmp$start),
score = tmp$score,
strand = tmp$strand,
seqlengths = seqlengths(genome)
)
tmp = promoters(tmp,upstream = 1,downstream = 1)
Warning in valid.GenomicRanges.seqinfo(x, suggest.trim = TRUE) :
GRanges object contains 25 out-of-bound ranges located on sequences chr1,
chr2, chr3, chr4, chr5, chr6, chr7, chr8, chr9, chr10, chr11, chr12,
chr13, chr14, chr15, chr16, chr17, chr18, chr19, chr20, chr21, chr22,
chrX, chrY, and chrMT. Note that ranges located on a sequence whose length
is unknown (NA) or on a circular sequence are not considered out-of-bound
(use seqlengths() and isCircular() to get the lengths and circularity
flags of the underlying sequences). You can use trim() to trim these
ranges. See ?`trim,GenomicRanges-method` for more information.
tmp = tmp[width(trim(tmp)) == 2]
#tmp$dinucleotide = as.data.frame(getSeq(genome,tmp))$x
tmp$dinucleotide = as.character(getSeq(genome, tmp))
A = as.data.frame(tmp)[,c("score","dinucleotide")]
dinucleotide = unique(A$dinucleotide)
B = data.frame(dinucleotide=dinucleotide,sum_score=NA)
for (i in dinucleotide) {
B[which(B$dinucleotide==i),]$sum_score<-sum(A[which(A$dinucleotide==i),]$score)
}
B$proportion = B$sum_score/sum(B$sum_score)
B = B[order(B$proportion,decreasing=F),]
B$dinucleotide = factor(B$dinucleotide,levels = B$dinucleotide)
ggplot(data=B,
aes(x=factor(dinucleotide),
y=(proportion*100),
fill=dinucleotide)) +
geom_bar(stat="identity",
colour = "black",
linewidth = 0.3,
position=position_dodge()) +
ggtitle(paste0("Dominant TSS dinucleotide -/+ 1 bp proportion weighted by the sum of dominant TSS score per sample")) +
xlab("Initiator dinucleotide") +
ylab("Proportion of total per sample") +
coord_flip() +
theme_bw() +
theme(text = element_text(size=10),
legend.key.size = unit(0.8,"cm"),
legend.position = 'none') +
scale_fill_viridis(option = "plasma",
discrete = TRUE)
#ggsave(filename = paste0(image_path,"TAG_weighted.pdf"),width = 12,height=10)
head(tmp$seqnames)
seqnames(genome)
setdiff(seqlevels(tmp), seqlevels(genome))
plotReverseCumulatives(star_TSS, fitInRange = c(5, 1000))
pdf("/Users/pavel/Desktop/plotReverseCumulatives.pdf", width = 13, height = 7)
#par(mfrow = c(5, 6), mar = c(4, 4, 2, 1))
plotReverseCumulatives(star_TSS, fitInRange = c(5, 1000))
dev.off()
# bad
pdf("/Users/pavel/Desktop/plotCorrelation2.pdf", width = 20, height = 20)
corr.m <- plotCorrelation2(star_TSS, samples = "all",
tagCountThreshold = 1, applyThresholdBoth = FALSE,
method = "pearson")
dev.off()
#star_TSS = readRDS("/Users/pavel/Desktop/SoftclipG_CAGEexp_T2T_CHM13v2.0_annotated.RDS")
tag_clusters = distclu(star_TSS, maxDist = 20, keepSingletonsAbove = 5)
Clustering...
-> ArticularChondrocyteKneeJoint
-> BCell
library(BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0)
genome = BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0
tmp = as.data.frame(CTSScoordinatesGR(object = star_TSS), row.names = NULL)
scores_df = as.data.frame(CTSSnormalizedTpmDF(object = star_TSS))
scores_df$seqnames = tmp$seqnames
scores_df$start = tmp$start
scores_df$strand = tmp$strand
seqlevels(genome) = paste0("chr", seqlevels(genome))
scores_df$seqnames = as.character(scores_df$seqnames)
scores_df$seqnames[scores_df$seqnames == "chrM"] = "chrMT"
valid_seqnames = seqlevels(genome)
keep_rows = scores_df$seqnames %in% valid_seqnames
scores_df = scores_df[keep_rows, ]
tmp = tmp[keep_rows, ]
tmp_gr = GRanges(
seqnames = scores_df$seqnames,
ranges = IRanges(start = scores_df$start, end = scores_df$start),
strand = scores_df$strand,
seqlengths = seqlengths(genome)
)
tmp_gr = promoters(tmp_gr, upstream = 1, downstream = 1)
width_ok = width(trim(tmp_gr)) == 2
tmp_gr = tmp_gr[width_ok]
scores_df = scores_df[width_ok, ]
scores_df$dinucleotide = as.character(getSeq(genome, tmp_gr))
scores_df[] <- lapply(scores_df, function(x) if (is.numeric(x)) as.single(x) else x)
sample_cols = setdiff(colnames(scores_df), c("seqnames", "start", "strand", "dinucleotide"))
# Initialize result
result_list = list()
# Loop through sample columns and compute proportions per dinucleotide
for (sample in sample_cols) {
df = scores_df[, c("dinucleotide", sample)]
colnames(df) = c("dinucleotide", "score")
df_sum = df %>%
group_by(dinucleotide) %>%
summarise(sum_score = sum(score), .groups = "drop") %>%
mutate(sample = sample,
proportion = sum_score / sum(sum_score))
result_list[[sample]] = df_sum
}
B = bind_rows(result_list)
dinuc_order = B %>%
group_by(dinucleotide) %>%
summarise(total_prop = sum(proportion)) %>%
arrange(total_prop) %>%
pull(dinucleotide)
B$dinucleotide = factor(B$dinucleotide, levels = dinuc_order)
B$sample = factor(B$sample, levels = sample_cols)
net_cage_samples = c("H9hESC", "K562", "HepG2", "HeLaS3", "GM12878", "MCF7")
B$experiment = ifelse(B$sample %in% net_cage_samples, "NET-CAGE", "CAGE")
B$sample_ordered = reorder_within(B$sample, B$proportion, B$dinucleotide, fun = identity)
#ggplot(B, aes(x = dinucleotide, y = proportion * 100, group=sample_ordered, fill = sample)) +
ggplot(B, aes(x = dinucleotide, y = proportion * 100, group=sample_ordered, fill = experiment)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), linewidth = 0.3) +
ggtitle("Dominant TSS dinucleotide -/+ 1 bp proportion weighted by the sum of dominant TSS score per sample") +
xlab("Initiator dinucleotide") +
ylab("Proportion of total per sample (%)") +
coord_flip() +
theme_bw() +
theme(text = element_text(size = 10),
legend.key.size = unit(0.8, "cm")) +
scale_fill_manual(values = c("CAGE" = "#4cc9f0", "NET-CAGE" = "#f07167"))
#ggsave(filename = paste0("/Users/pavel/Desktop/","dom_tss_experiment.pdf"),width = 6,height=5)
#scale_fill_viridis(discrete = TRUE, option = "plasma")
B_net = B %>%
filter(experiment == "NET-CAGE")
dinuc_order = B_net %>%
group_by(dinucleotide) %>%
summarise(total_prop = sum(proportion)) %>%
arrange(total_prop) %>%
pull(dinucleotide)
B_net$dinucleotide = factor(B_net$dinucleotide, levels = dinuc_order)
B_net$sample = factor(B_net$sample, levels = sample_cols)
B_net$sample_ordered = reorder_within(B_net$sample, B_net$proportion, B_net$dinucleotide, fun = identity)
B_cage = B %>%
filter(experiment == "CAGE")
B_cage1 = B_cage %>%
slice(1:176)
dinuc_order = B_cage1 %>%
group_by(dinucleotide) %>%
summarise(total_prop = sum(proportion)) %>%
arrange(total_prop) %>%
pull(dinucleotide)
B_cage1$dinucleotide = factor(B_cage1$dinucleotide, levels = dinuc_order)
B_cage1$sample = factor(B_cage1$sample, levels = sample_cols)
B_cage1$sample_ordered = reorder_within(B_cage1$sample, B_cage1$proportion, B_cage1$dinucleotide, fun = identity)
B_cage2 = B_cage %>%
slice(177:336)
dinuc_order = B_cage2 %>%
group_by(dinucleotide) %>%
summarise(total_prop = sum(proportion)) %>%
arrange(total_prop) %>%
pull(dinucleotide)
B_cage2$dinucleotide = factor(B_cage2$dinucleotide, levels = dinuc_order)
B_cage2$sample = factor(B_cage2$sample, levels = sample_cols)
B_cage2$sample_ordered = reorder_within(B_cage2$sample, B_cage2$proportion, B_cage2$dinucleotide, fun = identity)
ggplot(B_net, aes(x = dinucleotide, y = proportion * 100, group=sample, fill = sample)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), linewidth = 0.3) +
ggtitle("Dominant TSS dinucleotide -/+ 1 bp proportion weighted by the sum of dominant TSS score per sample") +
xlab("Initiator dinucleotide") +
ylab("Proportion of total per sample (%)") +
coord_flip() +
theme_bw() +
theme(text = element_text(size = 10),
legend.key.size = unit(0.8, "cm"))
ggsave(filename = paste0("/Users/pavel/Desktop/","dom_tss_net.pdf"),width = 6,height=5)
ggplot(B_cage1, aes(x = dinucleotide, y = proportion * 100, group=sample, fill = sample)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), linewidth = 0.3) +
ggtitle("Dominant TSS dinucleotide -/+ 1 bp proportion weighted by the sum of dominant TSS score per sample") +
xlab("Initiator dinucleotide") +
ylab("Proportion of total per sample (%)") +
coord_flip() +
theme_bw() +
theme(text = element_text(size = 10),
legend.key.size = unit(0.8, "cm"))
ggsave(filename = paste0("/Users/pavel/Desktop/","dom_tss_cage1.pdf"),width = 8,height=5)
ggplot(B_cage2, aes(x = dinucleotide, y = proportion * 100, group=sample, fill = sample)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), linewidth = 0.5) +
ggtitle("Dominant TSS dinucleotide -/+ 1 bp proportion weighted by the sum of dominant TSS score per sample") +
xlab("Initiator dinucleotide") +
ylab("Proportion of total per sample (%)") +
coord_flip() +
theme_bw() +
theme(text = element_text(size = 10),
legend.key.size = unit(0.8, "cm"))
ggsave(filename = paste0("/Users/pavel/Desktop/","dom_tss_cage2.pdf"),width = ,height=5)
Saving 6.58 x 5 in image
ggplot(B, aes(x = sample, y = proportion * 100, fill = experiment)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8), linewidth = 0.3) +
facet_wrap(~ dinucleotide, nrow = 1, strip.position = "bottom") +
ggtitle("Dominant TSS dinucleotide -/+ 1 bp proportion weighted by the sum of dominant TSS score per sample") +
xlab("Sample") +
ylab("Proportion of total per sample (%)") +
theme_bw() +
theme(text = element_text(size = 10),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "top",
strip.placement = "outside",
strip.text = element_text(size = 9),
panel.spacing = unit(1, "lines")) +
scale_fill_manual(values = c("CAGE" = "#21908CFF", "NET-CAGE" = "#FDE725FF"))